app.py

import html
import json
import os
from typing import List

# Load .env file automatically if present
try:
    from dotenv import load_dotenv
    load_dotenv()
except ImportError:
    pass  # python-dotenv not installed; set env vars manually or pip install python-dotenv

import gradio as gr
import pandas as pd

from topic_pipeline import (
    OUTPUT_DIR,
    parse_notebooklm_tccm_text,
    run_complete_pipeline,
    write_tccm_dual_validation,
)


os.makedirs(OUTPUT_DIR, exist_ok=True)


def _exists(name: str) -> bool:
    return os.path.exists(os.path.join(OUTPUT_DIR, name))


def _load_json(name: str):
    with open(os.path.join(OUTPUT_DIR, name), "r", encoding="utf-8") as f:
        return json.load(f)


def _download_files() -> List[str]:
    names = [
        "comparison.csv",
        "taxonomy_map.json",
        "topic_model_report.md",
        "narrative.txt",
        "cluster_optimization_log.csv",
        "llm_council_validation.csv",
        "tccm_validation.csv",
        "tccm_dual_validation.csv",
        "notebooklm_extraction.csv",
        "compliance_checklist.csv",
        "compliance_checklist.json",
        "run_metadata.json",
        "combined_labels.json",
    ]
    return [os.path.join(OUTPUT_DIR, name) for name in names if _exists(name)]


def _phase_html() -> str:
    phases = [
        ("Corpus", _exists("corpus_config.json")),
        ("Embeddings", _exists("combined_emb.npy")),
        ("Optimization", _exists("cluster_optimization_log.csv")),
        ("Clusters", _exists("combined_labels.json")),
        ("Council", _exists("llm_council_validation.csv")),
        ("TCCM", _exists("tccm_validation.csv")),
        ("Compliance", _exists("compliance_checklist.csv")),
        ("Report", _exists("topic_model_report.md")),
    ]
    chips = []
    for name, done in phases:
        cls = "done" if done else "pending"
        mark = "✓" if done else "·"
        chips.append(
            f"<span class='eis-phase-chip {cls}'>{mark} {name}</span>"
        )
    return "<div style='display:flex;gap:8px;flex-wrap:wrap;padding:4px 0'>" + "".join(chips) + "</div>"


def _cluster_table():
    if not _exists("combined_labels.json"):
        return []
    rows = []
    for s in _load_json("combined_labels.json"):
        rows.append([
            s.get("cluster_id"),
            s.get("label"),
            s.get("category"),
            s.get("paper_count"),
            s.get("confidence"),
            s.get("agreement_score"),
            "; ".join(s.get("keywords", [])[:8]),
            " | ".join(s.get("top_titles", [])[:3]),
            s.get("reasoning", ""),
        ])
    return rows


def _council_table():
    path = os.path.join(OUTPUT_DIR, "llm_council_validation.csv")
    if not os.path.exists(path):
        return []
    return pd.read_csv(path).head(120)


def _council_viz_html() -> str:
    path = os.path.join(OUTPUT_DIR, "llm_council_validation.csv")
    if not os.path.exists(path):
        return (
            "<div class='council-empty'>Run the pipeline to activate the LLM Council "
            "validation board.</div>"
        )

    df = pd.read_csv(path)
    if df.empty:
        return "<div class='council-empty'>Council validation file is empty.</div>"

    grouped = list(df.groupby(["cluster_id", "final_label"], sort=False))[:6]
    rows = []
    avg_agreement = float(df["agreement_score"].mean()) if "agreement_score" in df else 0
    avg_confidence = float(df["confidence"].mean()) if "confidence" in df else 0
    llm_member_present = df["member"].astype(str).str.contains("LLM|Mistral", case=False, regex=True).any()
    llm_status = "Mistral LLM active" if llm_member_present else "Local semantic fallback active"

    for (cluster_id, final_label), group in grouped:
        votes = []
        for _, row in group.iterrows():
            member = html.escape(str(row.get("member", "")))
            label = html.escape(str(row.get("member_label", "")))
            method = html.escape(str(row.get("method", "")))
            votes.append(
                "<div class='council-vote'>"
                "<div class='vote-dot'></div>"
                f"<div><strong>{member}</strong><span>{label}</span><small>{method}</small></div>"
                "</div>"
            )
        confidence = int(float(group["confidence"].iloc[0]) * 100)
        agreement = int(float(group["agreement_score"].iloc[0]) * 100)
        rows.append(
            "<div class='council-cluster'>"
            "<div class='cluster-head'>"
            f"<span>Cluster {html.escape(str(cluster_id))}</span>"
            f"<strong>{html.escape(str(final_label))}</strong>"
            "</div>"
            "<div class='council-flow'>"
            + "".join(votes) +
            "<div class='final-label'>"
            "<small>Accepted label</small>"
            f"<strong>{html.escape(str(final_label))}</strong>"
            f"<span>{confidence}% confidence | {agreement}% agreement</span>"
            "</div>"
            "</div>"
            "</div>"
        )

    return (
        "<div class='council-board'>"
        "<div class='council-top'>"
        "<div><h3>LLM Council Validation Running In-App</h3>"
        "<p>Three independent validators inspect each cluster label, compare votes, "
        "and write the accepted label plus agreement score into the export file.</p></div>"
        "<div class='council-metrics'>"
        f"<div><strong>{len(df['cluster_id'].unique())}</strong><span>clusters checked</span></div>"
        f"<div><strong>{int(avg_agreement * 100)}%</strong><span>avg agreement</span></div>"
        f"<div><strong>{int(avg_confidence * 100)}%</strong><span>avg confidence</span></div>"
        f"<div><strong>{html.escape(llm_status)}</strong><span>council mode</span></div>"
        "</div></div>"
        "<div class='council-lane'>"
        "<div class='pulse-node'>1<br><span>Keyword Extractor</span></div>"
        "<div class='pulse-line'></div>"
        "<div class='pulse-node'>2<br><span>PAJAIS Mapper</span></div>"
        "<div class='pulse-line'></div>"
        "<div class='pulse-node'>3<br><span>LLM / Semantic Judge</span></div>"
        "<div class='pulse-line'></div>"
        "<div class='pulse-node final'>OK<br><span>Validated Label</span></div>"
        "</div>"
        + "".join(rows) +
        "</div>"
    )


def _optimizer_table():
    path = os.path.join(OUTPUT_DIR, "cluster_optimization_log.csv")
    if not os.path.exists(path):
        return []
    df = pd.read_csv(path)
    cols = [
        c for c in [
            "algorithm",
            "umap_n_neighbors",
            "umap_n_components",
            "hdbscan_min_cluster_size",
            "hdbscan_min_samples",
            "n_clusters",
            "noise_ratio",
            "min_size",
            "max_size",
            "too_small",
            "too_large",
            "silhouette_cosine",
            "score",
            "optimizer_recommendation",
        ] if c in df.columns
    ]
    return df[cols].head(80)


def _tccm_table():
    path = os.path.join(OUTPUT_DIR, "tccm_validation.csv")
    if not os.path.exists(path):
        return []
    return pd.read_csv(path).head(100)


def _tccm_dual_table():
    path = os.path.join(OUTPUT_DIR, "tccm_dual_validation.csv")
    if not os.path.exists(path):
        return []
    return pd.read_csv(path).head(100)


def _compliance_table():
    path = os.path.join(OUTPUT_DIR, "compliance_checklist.csv")
    if not os.path.exists(path):
        return []
    return pd.read_csv(path)


def _compliance_html() -> str:
    path = os.path.join(OUTPUT_DIR, "compliance_checklist.csv")
    if not os.path.exists(path):
        return (
            "<div class='compliance-empty'>Run the pipeline to generate the professor-requirement "
            "compliance checklist.</div>"
        )
    df = pd.read_csv(path)
    color_map = {
        "PASS": "#0f766e",
        "FAIL": "#b91c1c",
        "CONFIG_REQUIRED": "#b45309",
        "ENV_FALLBACK": "#b45309",
        "INPUT_REQUIRED": "#b45309",
        "PARTIAL": "#7c3aed",
        "MANUAL_REQUIRED": "#475569",
        "REVIEW": "#7c3aed",
    }
    rows = []
    for _, row in df.iterrows():
        status = str(row.get("Status", "REVIEW"))
        color = color_map.get(status, "#475569")
        rows.append(
            "<div class='compliance-row'>"
            f"<span style='background:{color}'>{html.escape(status)}</span>"
            f"<strong>{html.escape(str(row.get('Requirement', '')))}</strong>"
            f"<p>{html.escape(str(row.get('Evidence', '')))}</p>"
            f"<small>{html.escape(str(row.get('File', '')))}</small>"
            "</div>"
        )
    return (
        "<div class='compliance-board'>"
        "<h3>Professor Requirement Compliance Checklist</h3>"
        "<p>This separates completed app evidence from items that still need API secrets, "
        "NotebookLM/full-text inputs, or mentor approval.</p>"
        "<div class='compliance-grid'>" + "".join(rows) + "</div></div>"
    )


def _tccm_dual_status_html() -> str:
    path = os.path.join(OUTPUT_DIR, "tccm_dual_validation.csv")
    if not os.path.exists(path):
        return (
            "<div class='compliance-empty'>Upload NotebookLM and second-LLM extraction CSVs "
            "to generate TCCM dual validation.</div>"
        )
    df = pd.read_csv(path)
    status_col = "Final_TCCM_Compliance_Status"
    if status_col not in df.columns:
        return "<div class='compliance-empty'>TCCM dual validation is pending source uploads.</div>"
    counts = df[status_col].value_counts().to_dict()
    cards = []
    for status, count in counts.items():
        ok = "COMPLIANT" in str(status)
        color = "#0f766e" if ok else "#b45309"
        cards.append(
            f"<div class='tccm-card'><strong style='color:{color}'>{count}</strong>"
            f"<span>{html.escape(str(status))}</span></div>"
        )
    return (
        "<div class='tccm-status'><h3>TCCM Dual Validation Status</h3>"
        "<p>Required by email: NotebookLM extraction plus another LLM/extraction method. "
        "This screen reconciles those files with regex/semantic extraction.</p>"
        "<div>" + "".join(cards) + "</div></div>"
    )


def _on_tccm_dual_validate(notebook_file, second_file):
    notebook_path = notebook_file if isinstance(notebook_file, str) else getattr(notebook_file, "name", "")
    second_path = second_file if isinstance(second_file, str) else getattr(second_file, "name", "")
    write_tccm_dual_validation(notebook_path, second_path)
    return _tccm_dual_status_html(), _tccm_dual_table(), _download_files()


def _on_notebooklm_paste(notebook_text):
    if not str(notebook_text or "").strip():
        return (
            "<div class='compliance-empty'>Paste the NotebookLM table text first.</div>",
            _tccm_dual_table(),
            _download_files(),
        )
    notebook_path = parse_notebooklm_tccm_text(notebook_text)
    write_tccm_dual_validation(notebook_path, "")
    count = len(pd.read_csv(notebook_path)) if os.path.exists(notebook_path) else 0
    status = (
        f"<div class='tccm-status'><h3>NotebookLM Paste Imported</h3>"
        f"<p>Parsed {count} NotebookLM rows into <code>outputs/notebooklm_extraction.csv</code>. "
        "Merged with the independent regex/semantic extractor in "
        "<code>outputs/tccm_dual_validation.csv</code>. Upload a second-LLM CSV as well "
        "for full NotebookLM + second LLM compliance.</p></div>"
        + _tccm_dual_status_html()
    )
    return status, _tccm_dual_table(), _download_files()


def _chart_iframe(name: str) -> str:
    path = os.path.join(OUTPUT_DIR, "combined_charts", name)
    if not os.path.exists(path):
        return (
            "<div style='height:320px;display:grid;place-items:center;"
            "background:#0f172a;color:#94a3b8;border-radius:8px'>"
            "Run the pipeline to generate this chart.</div>"
        )
    with open(path, "r", encoding="utf-8") as f:
        srcdoc = f.read().replace("&", "&amp;").replace('"', "&quot;")
    return (
        f"<iframe srcdoc=\"{srcdoc}\" width='100%' height='500' "
        "style='border:0;border-radius:8px;background:#0f172a'></iframe>"
    )


def _cards_html() -> str:
    if not _exists("combined_labels.json"):
        return (
            "<div style='padding:32px;color:#475569;background:#0a1220;"
            "border:1px dashed #1e3a5c;border-radius:12px;font-family:Inter,sans-serif'>"
            "Clusters will appear here after a complete run.</div>"
        )
    cards = []
    for s in _load_json("combined_labels.json"):
        evidence = html.escape(" | ".join(s.get("top_titles", [])[:3]))
        label = html.escape(s.get("label", "Cluster"))
        category = html.escape(s.get("category", "Unmapped"))
        keywords = html.escape(", ".join(s.get("keywords", [])[:8]))
        conf = int(float(s.get("confidence", 0)) * 100)
        cards.append(
            "<div class='eis-cluster-card'>"
            f"<div style='font-size:15px;font-weight:800;color:#f1f5f9;font-family:Outfit,sans-serif'>{label}</div>"
            f"<div style='font-size:11px;color:#60a5fa;margin-top:4px;font-weight:600;letter-spacing:0.3px'>{category}</div>"
            f"<div style='margin-top:10px;font-size:12px;color:#475569'>"
            f"{s.get('paper_count', 0)} papers &nbsp;·&nbsp; confidence <span style='color:#fbbf24;font-weight:700'>{conf}%</span></div>"
            f"<div style='margin-top:8px;font-size:12px;color:#1d4ed8;font-weight:600'>{keywords}</div>"
            f"<div style='margin-top:10px;font-size:11px;color:#334155;line-height:1.5'>{evidence}</div>"
            "</div>"
        )
    return (
        "<div style='display:grid;grid-template-columns:repeat(auto-fit,minmax(280px,1fr));"
        "gap:14px;padding:4px 0'>" + "".join(cards) + "</div>"
    )


def _summary_markdown(result=None) -> str:
    if result is None and not _exists("run_metadata.json"):
        return (
            "Upload the Scopus CSV and click **Run Complete Pipeline**. "
            "The app will generate paper-level Title+Abstract+DOI embeddings, optimize "
            "UMAP/HDBSCAN clustering, label 15-25 clusters through an in-app council, "
            "map them to PAJAIS, and export TCCM validation files."
        )
    meta = result or {}
    if not meta:
        meta = {
            "parameters": _load_json("run_metadata.json").get("selected_parameters", {}),
            "embedding": _load_json("run_metadata.json").get("embedding", {}),
            "clusters": _load_json("combined_labels.json") if _exists("combined_labels.json") else [],
            "taxonomy": _load_json("taxonomy_map.json") if _exists("taxonomy_map.json") else {},
            "config": _load_json("corpus_config.json") if _exists("corpus_config.json") else {},
        }
    params = meta.get("parameters", {})
    emb = meta.get("embedding", {})
    tax = meta.get("taxonomy", {}).get("coverage_stats", {})
    cfg = meta.get("config", {})
    return (
        f"**Run complete.** Analysed {cfg.get('rows', 'N/A')} papers from "
        f"{cfg.get('journal', 'the corpus')} ({cfg.get('year_min')} to {cfg.get('year_max')}).\n\n"
        f"Selected clustering: `{params.get('algorithm')}` with "
        f"`{params.get('n_clusters')}` clusters, min size `{params.get('min_size')}`, "
        f"max size `{params.get('max_size')}`, noise ratio `{params.get('noise_ratio')}`.\n\n"
        f"Embedding: `{emb.get('embedding_model')}`. PAJAIS mapped: "
        f"`{tax.get('mapped', 0)}`; novel: `{tax.get('novel', 0)}`. "
        "Download the optimizer log and council validation for the final submission appendix."
    )


def _run(file_obj):
    if file_obj is None:
        return (
            "Upload a CSV first.",
            _phase_html(),
            _cluster_table(),
            _cards_html(),
            _optimizer_table(),
            _compliance_html(),
            _compliance_table(),
            _council_viz_html(),
            _council_table(),
            _tccm_table(),
            _chart_iframe("intertopic_map.html"),
            _chart_iframe("bar_chart.html"),
            _chart_iframe("treemap.html"),
            _download_files(),
        )
    filepath = file_obj if isinstance(file_obj, str) else file_obj.name
    result = run_complete_pipeline(filepath)
    return (
        _summary_markdown(result),
        _phase_html(),
        _cluster_table(),
        _cards_html(),
        _optimizer_table(),
        _compliance_html(),
        _compliance_table(),
        _council_viz_html(),
        _council_table(),
        _tccm_table(),
        _chart_iframe("intertopic_map.html"),
        _chart_iframe("bar_chart.html"),
        _chart_iframe("treemap.html"),
        result["deliverables"],
    )


def _refresh():
    return (
        _summary_markdown(),
        _phase_html(),
        _cluster_table(),
        _cards_html(),
        _optimizer_table(),
        _compliance_html(),
        _compliance_table(),
        _council_viz_html(),
        _council_table(),
        _tccm_table(),
        _chart_iframe("intertopic_map.html"),
        _chart_iframe("bar_chart.html"),
        _chart_iframe("treemap.html"),
        _download_files(),
    )


CSS = """
@import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700;800&family=Outfit:wght@700;800;900&display=swap');

* { box-sizing: border-box; }

body, .gradio-container {
  background: #070b14 !important;
  font-family: 'Inter', sans-serif !important;
}
.gradio-container { max-width: 1400px !important; }

/* ── hero header ── */
.eis-hero {
  background: linear-gradient(135deg, #0d1b2e 0%, #0a2240 40%, #0c1a35 100%);
  border: 1px solid #1a3a5c;
  border-radius: 16px;
  padding: 36px 40px;
  margin-bottom: 8px;
  position: relative;
  overflow: hidden;
}
.eis-hero::before {
  content: '';
  position: absolute;
  top: -60px; right: -60px;
  width: 280px; height: 280px;
  background: radial-gradient(circle, rgba(245,158,11,0.12) 0%, transparent 70%);
  pointer-events: none;
}
.eis-hero::after {
  content: '';
  position: absolute;
  bottom: -40px; left: 30%;
  width: 200px; height: 200px;
  background: radial-gradient(circle, rgba(59,130,246,0.1) 0%, transparent 70%);
  pointer-events: none;
}
.eis-hero-badge {
  display: inline-block;
  background: rgba(245,158,11,0.15);
  border: 1px solid rgba(245,158,11,0.4);
  color: #fbbf24;
  font-size: 11px;
  font-weight: 700;
  letter-spacing: 2px;
  text-transform: uppercase;
  padding: 4px 12px;
  border-radius: 999px;
  margin-bottom: 14px;
}
.eis-hero h1 {
  font-family: 'Outfit', sans-serif;
  font-size: 38px;
  font-weight: 900;
  margin: 0 0 10px;
  background: linear-gradient(90deg, #f8fafc 0%, #93c5fd 60%, #fbbf24 100%);
  -webkit-background-clip: text;
  -webkit-text-fill-color: transparent;
  background-clip: text;
  line-height: 1.15;
}
.eis-hero p {
  color: #94a3b8;
  font-size: 15px;
  margin: 0;
  line-height: 1.6;
  max-width: 700px;
}
.eis-hero-stats {
  display: flex;
  gap: 28px;
  margin-top: 22px;
  flex-wrap: wrap;
}
.eis-stat {
  display: flex;
  flex-direction: column;
  gap: 2px;
}
.eis-stat strong {
  font-family: 'Outfit', sans-serif;
  font-size: 22px;
  font-weight: 800;
  color: #fbbf24;
}
.eis-stat span {
  font-size: 11px;
  color: #64748b;
  text-transform: uppercase;
  letter-spacing: 1px;
}

/* ── phase chips ── */
.eis-phase-chip {
  display: inline-flex;
  gap: 6px;
  align-items: center;
  padding: 6px 14px;
  border-radius: 999px;
  font-size: 12px;
  font-weight: 700;
  letter-spacing: 0.3px;
  transition: all 0.2s;
}
.eis-phase-chip.done {
  background: linear-gradient(135deg, #1d4ed8, #0891b2);
  color: #e0f2fe;
  box-shadow: 0 0 12px rgba(59,130,246,0.35);
}
.eis-phase-chip.pending {
  background: #0f172a;
  color: #475569;
  border: 1px solid #1e293b;
}

/* ── upload + run area ── */
.eis-upload-area {
  background: #0d1424;
  border: 1px solid #1e3a5c;
  border-radius: 12px;
  padding: 20px;
}

/* ── compliance ── */
.compliance-empty {
  padding: 28px;
  border: 1px dashed #1e3a5c;
  border-radius: 10px;
  background: #0a1220;
  color: #475569;
  font-family: 'Inter', sans-serif;
}
.compliance-board, .tccm-status {
  background: #0d1424;
  border: 1px solid #1e3a5c;
  border-radius: 12px;
  padding: 20px;
}
.compliance-board h3, .tccm-status h3 {
  margin: 0;
  color: #f1f5f9;
  font-family: 'Outfit', sans-serif;
  font-size: 20px;
  font-weight: 800;
}
.compliance-board p, .tccm-status p {
  color: #64748b;
  margin: 6px 0 16px;
  line-height: 1.5;
  font-size: 14px;
}
.compliance-grid {
  display: grid;
  grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
  gap: 10px;
}
.compliance-row {
  border: 1px solid #1e293b;
  border-radius: 10px;
  padding: 14px;
  background: #0a1220;
  transition: border-color 0.2s;
}
.compliance-row:hover { border-color: #2563eb; }
.compliance-row span {
  display: inline-block;
  color: white;
  font-size: 10px;
  font-weight: 800;
  padding: 3px 10px;
  border-radius: 999px;
  margin-bottom: 8px;
  letter-spacing: 0.5px;
}
.compliance-row strong { display: block; color: #e2e8f0; font-size: 13px; }
.compliance-row p { font-size: 12px; margin: 6px 0; color: #64748b; }
.compliance-row small { color: #334155; font-size: 11px; }

/* ── tccm cards ── */
.tccm-status > div {
  display: grid;
  grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
  gap: 10px;
}
.tccm-card {
  border: 1px solid #1e293b;
  border-radius: 10px;
  padding: 14px;
  background: #0a1220;
}
.tccm-card strong { display: block; font-size: 28px; color: #fbbf24; font-family: 'Outfit', sans-serif; }
.tccm-card span { color: #64748b; font-size: 12px; font-weight: 600; }

/* ── council board ── */
.council-empty {
  padding: 32px;
  border: 1px dashed #1e3a5c;
  color: #475569;
  border-radius: 12px;
  background: #0a1220;
}
.council-board {
  background: linear-gradient(160deg, #06101e 0%, #08162a 100%);
  color: #e5edf7;
  border-radius: 14px;
  padding: 22px;
  border: 1px solid #1a3354;
}
.council-top {
  display: grid;
  grid-template-columns: minmax(280px, 1.2fr) minmax(320px, 1fr);
  gap: 16px;
  align-items: start;
}
.council-top h3 {
  margin: 0;
  font-size: 20px;
  font-family: 'Outfit', sans-serif;
  font-weight: 800;
  color: #f1f5f9;
}
.council-top p { margin: 6px 0 0; color: #64748b; line-height: 1.5; font-size: 14px; }
.council-metrics { display: grid; grid-template-columns: repeat(2, minmax(140px, 1fr)); gap: 8px; }
.council-metrics div {
  background: #0c1e35;
  border: 1px solid #1a3a5c;
  border-radius: 10px;
  padding: 12px;
}
.council-metrics strong { display: block; color: #fbbf24; font-size: 20px; font-family: 'Outfit', sans-serif; }
.council-metrics span { color: #64748b; font-size: 12px; }
.council-lane {
  display: grid;
  grid-template-columns: 1fr 60px 1fr 60px 1fr 60px 1fr;
  gap: 8px;
  align-items: center;
  margin: 22px 0;
}
.pulse-node {
  min-height: 64px;
  display: grid;
  place-items: center;
  text-align: center;
  border: 1px solid #1d4ed8;
  background: linear-gradient(135deg, #0f2040, #0d1a35);
  border-radius: 10px;
  color: #93c5fd;
  font-weight: 800;
  animation: councilGlow 1.8s ease-in-out infinite;
}
.pulse-node span { display: block; font-size: 11px; font-weight: 600; color: #bfdbfe; margin-top: 3px; }
.pulse-node.final { border-color: #d97706; background: linear-gradient(135deg, #1c1000, #2a1800); color: #fbbf24; }
.pulse-line {
  height: 3px;
  border-radius: 999px;
  background: linear-gradient(90deg, #1d4ed8, #f59e0b, #1d4ed8);
  background-size: 220% 100%;
  animation: councilFlow 1.1s linear infinite;
}
.council-cluster {
  margin-top: 12px;
  padding: 14px;
  border: 1px solid #1a3354;
  border-radius: 10px;
  background: #080f1c;
}
.cluster-head { display: flex; justify-content: space-between; gap: 12px; color: #cbd5e1; margin-bottom: 10px; }
.cluster-head span { color: #60a5fa; font-weight: 800; }
.cluster-head strong { color: #f8fafc; }
.council-flow { display: grid; grid-template-columns: repeat(4, minmax(160px, 1fr)); gap: 8px; }
.council-vote, .final-label {
  border-radius: 10px;
  padding: 12px;
  background: #0c1e35;
  border: 1px solid #1a3a5c;
  min-height: 82px;
}
.council-vote { display: flex; gap: 8px; align-items: flex-start; }
.vote-dot {
  width: 10px; height: 10px;
  margin-top: 4px;
  border-radius: 50%;
  background: #f59e0b;
  box-shadow: 0 0 14px rgba(245,158,11,0.7);
  animation: councilBlink 1.2s ease-in-out infinite;
  flex: 0 0 auto;
}
.council-vote strong, .final-label strong { display: block; color: #e2e8f0; font-size: 13px; }
.council-vote span, .final-label span { display: block; color: #fbbf24; font-size: 12px; margin-top: 3px; }
.council-vote small, .final-label small { display: block; color: #475569; font-size: 11px; margin-top: 4px; line-height: 1.25; }
.final-label { border-color: #d97706; background: #130d00; }

/* ── cluster cards ── */
.eis-cluster-card {
  background: linear-gradient(135deg, #0d1830 0%, #0a1220 100%);
  border: 1px solid #1e3a5c;
  border-left: 4px solid #f59e0b;
  border-radius: 12px;
  padding: 18px;
  min-height: 180px;
  transition: transform 0.2s, box-shadow 0.2s, border-color 0.2s;
}
.eis-cluster-card:hover {
  transform: translateY(-2px);
  box-shadow: 0 8px 32px rgba(245,158,11,0.12);
  border-color: #2563eb;
}

/* ── Gradio overrides ── */
.gradio-container .tabs { background: transparent !important; }
.gradio-container .tab-nav button {
  color: #64748b !important;
  font-weight: 600 !important;
  border-bottom: 2px solid transparent !important;
  transition: all 0.2s !important;
}
.gradio-container .tab-nav button.selected {
  color: #fbbf24 !important;
  border-bottom-color: #f59e0b !important;
  background: transparent !important;
}
.gradio-container label { color: #94a3b8 !important; font-size: 13px !important; }
.gradio-container .prose { color: #94a3b8 !important; }

/* ── animations ── */
@keyframes councilFlow { from { background-position: 0% 0; } to { background-position: 220% 0; } }
@keyframes councilGlow { 0%, 100% { box-shadow: 0 0 0 rgba(29,78,216,0.2); } 50% { box-shadow: 0 0 22px rgba(245,158,11,0.3); } }
@keyframes councilBlink { 0%, 100% { opacity: .35; transform: scale(.75); } 50% { opacity: 1; transform: scale(1.15); } }
@keyframes fadeIn { from { opacity: 0; transform: translateY(8px); } to { opacity: 1; transform: translateY(0); } }

@media (max-width: 900px) {
  .council-top, .council-flow { grid-template-columns: 1fr; }
  .council-lane { grid-template-columns: 1fr; }
  .pulse-line { height: 18px; width: 3px; justify-self: center; }
  .eis-hero h1 { font-size: 26px; }
  .eis-hero-stats { gap: 16px; }
}
"""


with gr.Blocks(title="EIS Topic Intelligence", css=CSS, theme=gr.themes.Base()) as demo:
    gr.HTML(
        "<div class='eis-hero'>"
        "<div class='eis-hero-badge'>EIS &nbsp;·&nbsp; SPJIMR Research Analytics</div>"
        "<h1>EIS Topic Intelligence</h1>"
        "<p>Paper-level SPECTER2 / TF-IDF embeddings &nbsp;·&nbsp; UMAP + HDBSCAN optimised clustering"
        " &nbsp;·&nbsp; Live Mistral LLM council validation &nbsp;·&nbsp; PAJAIS taxonomy mapping &nbsp;·&nbsp; TCCM extraction</p>"
        "<div class='eis-hero-stats'>"
        "<div class='eis-stat'><strong>15–25</strong><span>Target clusters</span></div>"
        "<div class='eis-stat'><strong>3</strong><span>Council validators</span></div>"
        "<div class='eis-stat'><strong>25</strong><span>PAJAIS categories</span></div>"
        "<div class='eis-stat'><strong>100</strong><span>TCCM papers</span></div>"
        "</div>"
        "</div>"
    )
    phase = gr.HTML(value=_phase_html())

    with gr.Row():
        csv_file = gr.File(label="📂  Upload Scopus Journal CSV", file_types=[".csv"], scale=3)
        with gr.Column(scale=1):
            run_btn = gr.Button("▶  Run Complete Pipeline", variant="primary")
            refresh_btn = gr.Button("↻  Refresh Outputs")

    summary = gr.Markdown(value=_summary_markdown())

    with gr.Tabs():
        with gr.Tab("Clusters"):
            cluster_table = gr.Dataframe(
                headers=[
                    "Cluster ID", "Label", "PAJAIS Category", "Papers", "Confidence",
                    "Agreement", "Keywords", "Top 3 Titles", "Reasoning",
                ],
                value=_cluster_table(),
                wrap=True,
                interactive=False,
            )
            cluster_cards = gr.HTML(value=_cards_html())
        with gr.Tab("Optimization"):
            optimizer_table = gr.Dataframe(value=_optimizer_table(), wrap=True, interactive=False)
        with gr.Tab("Compliance"):
            compliance_panel = gr.HTML(value=_compliance_html())
            compliance_table = gr.Dataframe(value=_compliance_table(), wrap=True, interactive=False)
        with gr.Tab("Council Validation"):
            council_viz = gr.HTML(value=_council_viz_html())
            council_table = gr.Dataframe(value=_council_table(), wrap=True, interactive=False)
        with gr.Tab("TCCM Validation"):
            tccm_table = gr.Dataframe(value=_tccm_table(), wrap=True, interactive=False)
        with gr.Tab("Charts"):
            chart_map = gr.HTML(value=_chart_iframe("intertopic_map.html"))
            chart_bar = gr.HTML(value=_chart_iframe("bar_chart.html"))
            chart_tree = gr.HTML(value=_chart_iframe("treemap.html"))
        with gr.Tab("Downloads"):
            downloads = gr.File(value=_download_files(), label="Generated deliverables", file_count="multiple")

    outputs = [
        summary,
        phase,
        cluster_table,
        cluster_cards,
        optimizer_table,
        compliance_panel,
        compliance_table,
        council_viz,
        council_table,
        tccm_table,
        chart_map,
        chart_bar,
        chart_tree,
        downloads,
    ]
    run_btn.click(fn=_run, inputs=[csv_file], outputs=outputs, show_api=False, api_name=False)
    refresh_btn.click(fn=_refresh, inputs=None, outputs=outputs, show_api=False, api_name=False)


if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, ssr_mode=False)